This invention relates generally to computer based methods of modeling processes, and more specifically to methods for mapping business processes using an emergent model on a computer network.
Modeling is a process of describing the behavior of a system, possibly through the use of computers, such that the system's behavior can be predicted based upon varying inputs. Models can describe objects (entities) and their inter-relationships using mathematical equations. For example, a spreadsheet tool can be used to build a financial model of a particular business (system) to predict financial behavior, thus allowing a user to evaluate and choose among various solutions (designs).
Certain models are constructed from a set of modules (objects) that present an input and output interface. The inputs and outputs form connections and dependencies to use in integrating the objects to construct the model. Individual objects, although integrated, may be stored in a distributed fashion over a computer network. Objects themselves maybe comprised of multiple objects.
Different types of objects are used to relate information concerning different aspects of the system being modeled. Physical/mechanical modeling can produce solid models, surface models, three-dimensional models, two-dimensional models, and wire-frame models, and can be used to convey the physical aspects of a system within a defined space. Design modeling can be built to predict a system's behavior for a given set of design variables. Design models allow for the modification of their input variables to achieve a desired performance characteristic. Evaluation models can compare performance characteristics of a design model against specific value structures to access design alternatives.
The product design process is an example of a process that can include physical modeling, design modeling and evaluation modeling. Some people refer to these models in product design as Simulation Based Design. Product design is a complex and collaborative process that is often multi-disciplinary and multi-objective. These aspects of the product design process require a robust modeling framework.
An example of Simulation Based Design (“SBD”) is a program sponsored by the Defense Advanced Research Project Agency (“DARPA”) in cooperation with Lockheed Martin Missiles & Space company. The goal of SBD software is to enable an enterprise to perform “faster, better, cheaper” by establishing flexible, efficient communications channels among human participants and software tools across heterogeneous resources. This work is directed to developing a collaborative distributed computing infrastructure. Their work can be used as a framework for providing interoperability for a range of software (e.g., design/modeling) tools based on a Common Object Request Broker Architecture (“CORBA”) backplane. The NetBuilder application from Lockheed Martin Missiles & Space company is a framework for integrating and linking design and modeling components. An object-oriented repository for storing model components and a dynamic object server for maintaining various aspects of product development and interactions between multiple development disciplines. Legacy components within the NetBuilder framework are “wrapped” to encapsulate their capabilities, allowing legacy components to be linked with non-legacy components within the framework. Agents are also used within the NetBuilder framework to encapsulate information management paradigms, publish/subscribe information and manage automation of distributed workflow processes. NetBuilder acts as middleware to coordinate the development process.
MIT-DOME (Distributed Object-based Modeling and Evaluation) is a distributed modeling environment for integrated modeling that is used at the MIT CADLab (Senin, 1997; Pahng, 1998). In this environment, designers can easily build object-oriented models visualized as entity-relationship graphs. Both discrete and continuous variable types are allowed in the models. Models can be arranged into sub-models, these sub-models can be referenced in so called “catalogs” that allow for the selection of different sub-models when constructing a model. In MIT-DOME, model inputs with uncertain values can be defined as probability density functions, and these uncertainties are automatically propagated through the model using Monte Carlo simulation and other methods. MIT-DOME users also set goals or specifications and are provided with a design alternative which can be calculated. A built-in optimization tool, using a genetic algorithm as a solver, manipulates independent parameters and catalog choices to find an optimal tradeoff between model goals.